Принципи побудови комп'ютерних систем дистанційного тренування на основі аналізу відеопотоку

Basic construction principles of the remote training system were investigated. Operation of the system is based on the fact that the user tries to reproduce as accurately as repetitive movements, performed by the instructor. Algorithms for body movement image processing in the video stream were chosen so that the system was accessible to a wide range of users with home webcam and midrange computer. Point kinematic model of the human body movement was developed. The characteristic points of the human body in the video stream frames are determined based on the image skeletonization. According to the video stream data, for each characteristic point, its position, velocity and acceleration are calculated. Based on these data, a matrix of kinematic parameters for training and user movements is constructed. Quantitative comparison of two matrices is carried out using the Chebyshev and cosine similarity measures of vectors. Based on a comparison of the difference measures of vectors, recommendations are given to the user for correction of his movements. A prototype of the system was implemented as a software project. System testing has shown the correctness of its construction principles. Remote training system can be used in telemedicine for the rehabilitation of patients with musculoskeletal disorders, as well as remote sports training.Рассмотрены принципы построения систем дистанционной тренировки человека путем анализа видеопотока движений, которые выполняет пользователь системы и движений, выполненных  инструктором. Система базируется на двумерной проективной точечной модели движения человека. Для модели вычисляются кинематические параметры движения: координаты характерных точек, их мгновенные скорости и ускорения. Правильность заложенных принципов проверена путем  программной реализации системы.Розглянуто принципи побудови систем дистанційного тренування людини шляхом аналізу відеопотоку рухів, які виконує користувач системи та рухів, виконаних інструктором. Система базується на двохвимірній проективній точковій моделі руху людини. Для моделі обчислюються кінематичні параметри руху: координати характерних точок, їх миттєві швидкості та прискорення. Вірність закладених принципів перевірена шляхом програмної реалізації системи

Принципи побудови комп'ютерних систем дистанційного тренування на основі аналізу відеопотоку

Basic construction principles of the remote training system were investigated. Operation of the system is based on the fact that the user tries to reproduce as accurately as repetitive movements, performed by the instructor. Algorithms for body movement image processing in the video stream were chosen so that the system was accessible to a wide range of users with home webcam and midrange computer. Point kinematic model of the human body movement was developed. The characteristic points of the human body in the video stream frames are determined based on the image skeletonization. According to the video stream data, for each characteristic point, its position, velocity and acceleration are calculated. Based on these data, a matrix of kinematic parameters for training and user movements is constructed. Quantitative comparison of two matrices is carried out using the Chebyshev and cosine similarity measures of vectors. Based on a comparison of the difference measures of vectors, recommendations are given to the user for correction of his movements. A prototype of the system was implemented as a software project. System testing has shown the correctness of its construction principles. Remote training system can be used in telemedicine for the rehabilitation of patients with musculoskeletal disorders, as well as remote sports training.Рассмотрены принципы построения систем дистанционной тренировки человека путем анализа видеопотока движений, которые выполняет пользователь системы и движений, выполненных  инструктором. Система базируется на двумерной проективной точечной модели движения человека. Для модели вычисляются кинематические параметры движения: координаты характерных точек, их мгновенные скорости и ускорения. Правильность заложенных принципов проверена путем  программной реализации системы.Розглянуто принципи побудови систем дистанційного тренування людини шляхом аналізу відеопотоку рухів, які виконує користувач системи та рухів, виконаних інструктором. Система базується на двохвимірній проективній точковій моделі руху людини. Для моделі обчислюються кінематичні параметри руху: координати характерних точок, їх миттєві швидкості та прискорення. Вірність закладених принципів перевірена шляхом програмної реалізації системи

Recognition of human activities and expressions in video sequences using shape context descriptor

The recognition of objects and classes of objects is of importance in the field of computer vision due to its applicability in areas such as video surveillance, medical imaging and retrieval of images and videos from large databases on the Internet. Effective recognition of object classes is still a challenge in vision; hence, there is much interest to improve the rate of recognition in order to keep up with the rising demands of the fields where these techniques are being applied. This thesis investigates the recognition of activities and expressions in video sequences using a new descriptor called the spatiotemporal shape context. The shape context is a well-known algorithm that describes the shape of an object based upon the mutual distribution of points in the contour of the object; however, it falls short when the distinctive property of an object is not just its shape but also its movement across frames in a video sequence. Since actions and expressions tend to have a motion component that enhances the capability of distinguishing them, the shape based information from the shape context proves insufficient. This thesis proposes new 3D and 4D spatiotemporal shape context descriptors that incorporate into the original shape context changes in motion across frames. Results of classification of actions and expressions demonstrate that the spatiotemporal shape context is better than the original shape context at enhancing recognition of classes in the activity and expression domains